文献
J-GLOBAL ID:202202247282466352
整理番号:22A0976872
REANN:分子,反応性,および周期システムのためのPyTorchベースのエンドツーエンド多機能ディープニューラルネットワークパッケージ【JST・京大機械翻訳】
REANN: A PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems
著者 (3件):
Zhang Yaolong
(School of Chemistry and Materials Science, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China)
,
Xia Junfan
(School of Chemistry and Materials Science, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China)
,
Jiang Bin
(School of Chemistry and Materials Science, Department of Chemical Physics, Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, Anhui 230026, China)
資料名:
Journal of Chemical Physics
(Journal of Chemical Physics)
巻:
156
号:
11
ページ:
114801-114801-13
発行年:
2022年
JST資料番号:
C0275A
ISSN:
0021-9606
CODEN:
JCPSA6
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
アメリカ合衆国 (USA)
言語:
英語 (EN)